
see thread below:

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2007q3/000248.html

Basically, the way to specify a random effects stratification in lmer 
and nlme is:
(Pind and Dind are indicator variables for the respective groups that 
have different random effects variance)

fm.het <- lmer( dv ~ time.num*drug + ( 0 + Dind | Patient ) + ( 0 + Pind 
| Patient ), data=dat.new ) )
/fm.het <- lme( dv ~ time.num*drug, random = list(~ 0 + Pind //| 
Patient.new, //~ 0 + Dind //| Patient.new)//, data=dat.new )
/
If it was a crossover study and you wanted to have a covariance between 
the two groups you would have the following changes:

(0 + Dind + Pind | Patient )
/random = list(~ 0 + Pind //+ Dind | Patient.new)/






On 11/26/2012 05:13 AM, Gabriela Czanner wrote:
> *I have done lots of search to find out if lmer allows specification of
> heteroscedastic error variances. For example I wish to fit a simple model
> for response amplitude across 2 groups of diabetic patients, assuming
> different error variance int the two diabetic groups. I know how to specify
> heteroscedastic error variances in lme using weights option, but this does
> not seem to be implemented in lmer. Am I right?*
> **
> *Is there other way of getting around heteroscedastic errors by adding
> appropriate randome effects interaction term (as it was suggested on 17
> June 2011 in r-sig-mixed-models archive)?*
> **
> *Thank you,*
> *Gabriela*
>
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>
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> R-sig-mixed-models@r-project.org mailing list
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>


-- 
David Afshartous, Ph.D.
Research Associate Professor
PSTATŽ: ASA Accredited Professional Statistician
Department of Biostatistics
Vanderbilt University Medical Center


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